code: def plot_decision_function(X, y, clf, ax):
      """Plot the decision function of the classifier and the original data"""
      plot_step = 0.02
      x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx, yy = np.meshgrid(
          np.arange(x_min, x_max, plot_step),
          np.arange(y_min, y_max, plot_step),
      )
  
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      ax.contourf(xx, yy, Z, alpha=0.4)
      ax.scatter(X[:, 0], X[:, 1], alpha=0.8, c=y, edgecolor="k")
      ax.set_title(f"Resampling using {clf[0].__class__.__name__}")
  
file_path: imbalanced-learn/examples/combine/plot_comparison_combine.py
imports: import matplotlib.pyplot
  import seaborn
  from sklearn.datasets import make_classification
  from collections import Counter
  import numpy
  from sklearn.linear_model import LogisticRegression
  from imblearn.combine import SMOTEENN, SMOTETomek
  from imblearn.over_sampling import SMOTE
  from imblearn.pipeline import make_pipeline
label_desc: Use image node_id 2 for calling a global function with example usage: plot_decision_function(X, y, clf, ax) without return types